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Cited by 278

The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.

A lower bound on the mass of cold thermal dark matter from Planck

Open access
Bœhm, Céline, Dolan, Matthew J, McCabe, Christopher·2013·Journal of Cosmology and Astroparticle Physics

Abstract

We show that the new measurement of the effective number of neutrinos ( N eff ) by the Planck satellite can be used to set a robust lower bound on the mass of cold thermal dark matter of (MeV). Our limit applies if the dark matter remains in thermal equilibrium by coupling to electrons and photons or through interactions with neutrinos, and applies regardless of whether the dark matter annihilation cross-section is s -wave or p -wave. To illustrate our bounds we apply them to a model of a supersymmetric neutralino annihilating to neutrinos, via a light mixed left-right handed sneutrino mediator. While this scenario was not constrained by previous data, the Planck limits on N eff allow us to set a lower bound on the neutralino dark matter mass of 3.5 MeV.

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Crossref · 10.1088/1475-7516/2013/08/041

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Metadata

Authors
Bœhm, C., Dolan, M. J., & McCabe, C.
Venue
Journal of Cosmology and Astroparticle Physics
Published
2013-08-21
Volume
2013
Issue
08
Pages
041-041
ISSN
  • 1475-7508 (ISSN-L)
  • 1475-7516
OpenAlex
W1999486700
Cited by
278
References
80
FWCI
6.50
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A lower bound on the mass of cold thermal dark matter from Planck. https://app.cochat.ai/papers/10.1088/1475-7516/2013/08/041
The paper
Bœhm, C., Dolan, M. J., & McCabe, C. (2013). A lower bound on the mass of cold thermal dark matter from Planck. Journal of Cosmology and Astroparticle Physics. https://doi.org/10.1088/1475-7516/2013/08/041

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.